Mixed and Multilevel Models

Member Training: Mixture Models in Longitudinal Data Analysis

November 2nd, 2015 by

This webinar will present the steps to apply a type of latent class analysis on longitudinal data commonly known as growth mixture model (GMM). This family of models is a natural extension of the latent variable model. GMM combines longitudinal data analysis and Latent Class Analysis to extract the probabilities of each case to belong to latent trajectories with different model parameters. A brief (not exhaustive) list of steps to prepare, analyze and interpret GMM will be presented. A published case will be described to exemplify an application of GMM and its complexity.

Finally, an alternative approach to GMM will be presented where the longitudinal model approach is linear mixed effects (also known as hierarchical linear model or multilevel modeling). The idea is the same as in GMM using growth curve modeling, mainly that the latent class membership specifies specific unobserved trajectories. These models are equivalent to GMM and are sometimes referred to heterogeneous linear mixed effects, underlining the idea that the sample may not belong to one single homogeneous population, but potentially to a mixture of distributions.

Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.


Models for Repeated Measures Continuous, Categorical, and Count Data

April 6th, 2015 by

Lately, I’ve gotten a lot of questions about learning how to run models for repeated measures data that isn’t continuous.

Mostly categorical. But once in a while discrete counts.

A typical study is in linguistics or psychology where (more…)

Why Mixed Models are Harder in Repeated Measures Designs: G-Side and R-Side Modeling

February 25th, 2015 by

I have recently worked with two clients who were running generalized linear mixed models in SPSS.

Both had repeated measures experiments with a binary outcome.

The details of the designs were quite different, of course. But both had pretty complicated combinations of within-subjects factors.

Fortunately, both clients are intelligent, have a good background in statistical modeling, and are willing to do the work to learn how to do this. So in both cases, we made a lot of progress in just a couple meetings.

I found it interesting, through, that both were getting stuck on the same subtle point. It’s the same point I was missing for a long time in my own learning of mixed models.

Once I finally got it, a huge light bulb turned on. (more…)

How to Get SPSS GENLINMIXED Output Without the Model Viewer

September 26th, 2014 by

I love working with my clients.

I love working with my clients for many reasons, but one of them is I learn so much from them.

Just this week, one of my clients showed me how to get SPSS GENLINMIXED results without the Model Viewer.

She’s my new hero.

If you’ve ever used GENLINMIXED, the procedure for Generalized Linear Mixed Models, you know that the results automatically appear in this new Model Viewer.  (more…)

Model Building Strategies: Step Up and Top Down

September 19th, 2014 by

How should I build my model?Stage 2

I get this question a lot, and it’s difficult to answer at first glance–it depends too much on your particular situation.

There are really three parts to the approach to building a model: the strategy, the technique to implement that strategy, and the decision criteria used within the technique. (more…)

Examples for Writing up Results of Mixed Models

September 12th, 2014 by

One question I always get in my Repeated Measures Workshop is:

“Okay, now that I understand how to run a linear mixed model for my study, how do I write up the results?”

This is a great question.

There are many pieces of the linear mixed models output that are identical to those of any linear model–regression coefficients, F tests, means.

But there is also a lot that is new, like intraclass correlations and (more…)